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(Re)quantize existing QKeras model with model_quantize #19

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(Re)quantize existing QKeras model with model_quantize #19

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10 changes: 5 additions & 5 deletions qkeras/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -270,7 +270,7 @@ def model_quantize(model,
# Dense becomes QDense
# Activation converts activation functions

if layer["class_name"] == "Dense":
if layer["class_name"] in ["Dense", "QDense"]:
layer["class_name"] = "QDense"
# needs to add kernel/bias quantizers
kernel_quantizer = get_config(
Expand All @@ -290,7 +290,7 @@ def model_quantize(model,
else:
quantize_activation(layer_config, activation_bits)

elif layer["class_name"] in ["Conv1D", "Conv2D"]:
elif layer["class_name"] in ["Conv1D", "Conv2D", "QConv1D", "QConv2D"]:
q_name = "Q" + layer["class_name"]
layer["class_name"] = q_name
# needs to add kernel/bias quantizers
Expand All @@ -311,7 +311,7 @@ def model_quantize(model,
else:
quantize_activation(layer_config, activation_bits)

elif layer["class_name"] == "DepthwiseConv2D":
elif layer["class_name"] in ["DepthwiseConv2D", "QDepthwiseConv2D"]:
layer["class_name"] = "QDepthwiseConv2D"
# needs to add kernel/bias quantizers
depthwise_quantizer = get_config(quantizer_config, layer,
Expand All @@ -330,7 +330,7 @@ def model_quantize(model,
else:
quantize_activation(layer_config, activation_bits)

elif layer["class_name"] == "Activation":
elif layer["class_name"] in ["Activation", "QActivation"]:
quantizer = get_config(quantizer_config, layer, "QActivation")
# this is to avoid softmax from quantizing in autoq
if quantizer is None:
Expand All @@ -351,7 +351,7 @@ def model_quantize(model,
else:
quantize_activation(layer_config, activation_bits)

elif layer["class_name"] == "BatchNormalization":
elif layer["class_name"] in ["BatchNormalization", "QBatchNormalization"]:
layer["class_name"] = "QBatchNormalization"
# needs to add kernel/bias quantizers
gamma_quantizer = get_config(
Expand Down